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What is regression coefficient and correlation coefficient?
The strength of the linear relationship between the two variables in the regression equation is the correlation coefficient, r, and is always a value between -1 and 1, inclusive. The regression coefficient is the slope of the line of the regression equation.
What are the advantages of regression over correlation?
Correlation is a measure of association between two variables and the variables are not designated as dependent or independent. Simple regression is used to examine the relationship between one dependent and one independent variable. It goes beyond correlation by adding prediction capabilities.
Distinguish between correlation and regression?
Correlation is a measure of the degree of agreement in the changes (variances) in two or more variables. In the case of two variables, if one of them increases by the same amount for a unit increase in the other, then the correlation coefficient is +1. If one of them decreases by the same amount for a unit increase in the other, then the correlation coefficient is -1. Lesser agreement results in an intermediate value. Regression involves estimating or quantifying this relationship. It is very important to remember that correlation and regression measure only the linear relationship between variables. A symmetrical relationshup, for example, y = x2 between values of x with equal magnitudes (-a < x < a), has a correlation coefficient of 0, and the regression line will be a horizontal line. Also, a relationship found using correlation or regression need not be causal.
Is the regression equation a mathematical equation that defines the relationship between two variables?
No. It is an estimated equation that defines the best linear relationship between two variables (or their transforms). If the two variables, x and y were the coordinates of a circle, for example, any method for calculating the regression equation would fail hopelessly.
When a pair of variables have a positive correlation will the slope in the regression equation always be positive?
Yes
What is regression coefficient and correlation coefficient?
The strength of the linear relationship between the two variables in the regression equation is the correlation coefficient, r, and is always a value between -1 and 1, inclusive. The regression coefficient is the slope of the line of the regression equation.
What are the advantages of regression over correlation?
Correlation is a measure of association between two variables and the variables are not designated as dependent or independent. Simple regression is used to examine the relationship between one dependent and one independent variable. It goes beyond correlation by adding prediction capabilities.
Distinguish between correlation and regression?
Correlation is a measure of the degree of agreement in the changes (variances) in two or more variables. In the case of two variables, if one of them increases by the same amount for a unit increase in the other, then the correlation coefficient is +1. If one of them decreases by the same amount for a unit increase in the other, then the correlation coefficient is -1. Lesser agreement results in an intermediate value. Regression involves estimating or quantifying this relationship. It is very important to remember that correlation and regression measure only the linear relationship between variables. A symmetrical relationshup, for example, y = x2 between values of x with equal magnitudes (-a < x < a), has a correlation coefficient of 0, and the regression line will be a horizontal line. Also, a relationship found using correlation or regression need not be causal.
What is the difference between correlation and regression?
correlation we can do to find the strength of the variables. but regression helps to fit the best line
Is the regression equation a mathematical equation that defines the relationship between two variables?
No. It is an estimated equation that defines the best linear relationship between two variables (or their transforms). If the two variables, x and y were the coordinates of a circle, for example, any method for calculating the regression equation would fail hopelessly.
Difference between regression coefficient and correlation coefficient?
difference between correlation and regression?(1) The correlation answers the STRENGTH of linear association between paired variables, say X and Y. On the other hand, the regression tells us the FORM of linear association that best predicts Y from the values of X.(2a) Correlation is calculated whenever:* both X and Y is measured in each subject and quantify how much they are linearly associated.* in particular the Pearson's product moment correlation coefficient is used when the assumption of both X and Y are sampled from normally-distributed populations are satisfied* or the Spearman's moment order correlation coefficient is used if the assumption of normality is not satisfied.* correlation is not used when the variables are manipulated, for example, in experiments.(2b) Linear regression is used whenever:* at least one of the independent variables (Xi's) is to predict the dependent variable Y. Note: Some of the Xi's are dummy variables, i.e. Xi = 0 or 1, which are used to code some nominal variables.* if one manipulates the X variable, e.g. in an experiment.(3) Linear regression are not symmetric in terms of X and Y. That is interchanging X and Y will give a different regression model (i.e. X in terms of Y) against the original Y in terms of X.On the other hand, if you interchange variables X and Y in the calculation of correlation coefficient you will get the same value of this correlation coefficient.(4) The "best" linear regression model is obtained by selecting the variables (X's) with at least strong correlation to Y, i.e. >= 0.80 or
What do researchers use to represent graphically the correlation between two variables?
A linear regression
When a pair of variables have a positive correlation will the slope in the regression equation always be positive?
Yes
What is Full Regression?
Regression :The average Linear or Non linear relationship between Variables.
What is the importance of correlation and regression analysis in business decision making?
Correlation and regression analysis can help business to investigate the determinants of key variables such as their sales. Variations in a companies sales are likely to be related to variation in product prices,consumers,incomes,tastes and preference's multiple regression analysis can be used to investigate the nature of this relationship and correlation analysis can be used to test the goodness of fit. Regression can also be used to estimate the trend in a time series to make forecast
What are the three different types of correlation?
The three different types of correlation are positive correlation (both variables move in the same direction), negative correlation (variables move in opposite directions), and no correlation (variables show no relationship).
If coefficient of correlation r between two variables is zero does it mean that there is no relationship between the variables Justify your answer?
"If coefficient of correlation, "r" between two variables is zero, does it mean that there is no relationship between the variables? Justify your answer".
